基于高速铁路成本画像的定价预测模型研究  

Study on Pricing Prediction Model Based on High Speed Railway Cost Portrait

作　　者：任冲[1] 文琰杰 许旺土[3] REN Chong;WEN Yanjie;XU Wangtu(Traffic and Urban Planning Design Institute,China Railway Eryuan Engineering Group Co.,Ltd.,Chengdu 610031,Sichuan,China;School of Traffic and Transportation Engineering,Central South University,Changsha 410083,Hunan,China;School of Architecture and Civil Engineering,Xiamen University,Xiamen 361005,Fujian,China)

机构地区：[1]中铁二院工程集团有限责任公司交通与城市规划设计研究院,四川成都610031 [2]中南大学交通运输工程学院,湖南长沙410083 [3]厦门大学建筑与土木工程学院,福建厦门361005

出　　处：《铁道运输与经济》2023年第3期121-128,共8页Railway Transport and Economy

基　　金：中国铁路总公司科技研究开发计划课题(P2018X011)。

摘　　要：为了合理制定高速铁路项目票价,实现高速铁路可持续发展,提出一种基于高速铁路成本画像的定价预测模型,模型由潜在因子算法和卷积神经网络构成。对高速铁路可测量成本执行潜在因子算法,挖掘可测成本中包含的隐性特征,算法可以在有限的数据量下通过矩阵分解构造高速铁路成本画像,同时不需要以加载稀疏矩阵作为代价,有效减少硬件运行内存空间;将构造的高速铁路成本画像视为图像数据输入不同架构的卷积神经网络中,进行训练学习并预测定价。通过与多个基线模型进行比较,表明采用多个卷积层连接池化层预测高速铁路定价具备更高的精度,并研究新建高速铁路项目定价预测案例,结果表明预测结果与实际情况相符,为高速铁路定价预测研究提供参考。In order to set reasonable prices for high speed railway(HSR) projects and achieve sustainable development of HSRs, this study proposed a pricing prediction model based on the HSR cost portrait. The model consisted of a latent factor algorithm and a convolutional neural network. In order to uncover the latent features contained in the measurable costs of HSRs, the latent factor algorithm was applied to analyze these costs. The algorithm could construct an HSR cost portrait through matrix decomposition with limited data and avoid loading sparse matrices, which effectively reduced the memory space for operating hardware. The constructed HSR cost portrait was treated as image data and input into convolutional neural networks with different architectures for training and pricing prediction. The comparison with multiple baseline models shows that multiple convolutional layers connected with pooling layers have higher accuracy in predicting HSR prices. A case study on pricing prediction for a newly built HSR project shows that the predicted results are consistent with the actual situation, which provides a reference for the research on HSR pricing prediction.

关 键 词：高速铁路 定价预测 潜在因子算法 卷积神经网络 深度学习 

分 类 号：F530.5[经济管理—产业经济]